add ticker flow
This commit is contained in:
MuslemRahimi
parent
7cf63dd536
commit
3282315d7e
@ -68,130 +68,6 @@ async def get_stock_chart_data(ticker):
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def get_market_tide(interval_1m=True):
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res_list = []
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# Load the options flow JSON data only once.
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with open("json/options-flow/feed/data.json", "r") as file:
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all_data = orjson.loads(file.read())
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# We're processing SPY (the market tide) – if needed you could expand this list.
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tickers = ['SPY']
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# Use a single dictionary to track cumulative flows.
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delta_data = defaultdict(lambda: {
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'cumulative_net_call_premium': 0,
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'cumulative_net_put_premium': 0,
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'call_ask_vol': 0,
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'call_bid_vol': 0,
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'put_ask_vol': 0,
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'put_bid_vol': 0
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})
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# Process each ticker.
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for ticker in tqdm(tickers):
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# Filter and sort the data for the current ticker.
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data = [item for item in all_data if item['ticker'] == ticker]
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data.sort(key=lambda x: x['time'])
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for item in data:
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try:
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# Combine date and time, then truncate to the start of the minute.
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dt = datetime.strptime(f"{item['date']} {item['time']}", "%Y-%m-%d %H:%M:%S")
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dt = dt.replace(second=0, microsecond=0)
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if interval_1m:
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minute = dt.minute - (dt.minute % 1)
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dt = dt.replace(minute=minute)
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rounded_ts = dt.strftime("%Y-%m-%d %H:%M:%S")
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# Extract metrics.
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cost = float(item.get("cost_basis", 0))
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sentiment = item.get("sentiment", "")
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put_call = item.get("put_call", "")
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vol = int(item.get("volume", 0))
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# Update premium and volume metrics.
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if put_call == "Calls":
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if sentiment == "Bullish":
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delta_data[rounded_ts]['cumulative_net_call_premium'] += cost
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delta_data[rounded_ts]['call_ask_vol'] += vol
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elif sentiment == "Bearish":
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delta_data[rounded_ts]['cumulative_net_call_premium'] -= cost
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delta_data[rounded_ts]['call_bid_vol'] += vol
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elif put_call == "Puts":
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if sentiment == "Bullish":
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delta_data[rounded_ts]['cumulative_net_put_premium'] += cost
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delta_data[rounded_ts]['put_ask_vol'] += vol
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elif sentiment == "Bearish":
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delta_data[rounded_ts]['cumulative_net_put_premium'] -= cost
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delta_data[rounded_ts]['put_bid_vol'] += vol
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except Exception as e:
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print(f"Error processing item: {e}")
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# Calculate cumulative values over time.
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sorted_ts = sorted(delta_data.keys())
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cumulative = {
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'net_call_premium': 0,
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'net_put_premium': 0,
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'call_ask': 0,
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'call_bid': 0,
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'put_ask': 0,
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'put_bid': 0
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}
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for ts in sorted_ts:
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cumulative['net_call_premium'] += delta_data[ts]['cumulative_net_call_premium']
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cumulative['net_put_premium'] += delta_data[ts]['cumulative_net_put_premium']
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cumulative['call_ask'] += delta_data[ts]['call_ask_vol']
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cumulative['call_bid'] += delta_data[ts]['call_bid_vol']
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cumulative['put_ask'] += delta_data[ts]['put_ask_vol']
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cumulative['put_bid'] += delta_data[ts]['put_bid_vol']
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call_volume = cumulative['call_ask'] + cumulative['call_bid']
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put_volume = cumulative['put_ask'] + cumulative['put_bid']
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net_volume = (cumulative['call_ask'] - cumulative['call_bid']) - (cumulative['put_ask'] - cumulative['put_bid'])
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res_list.append({
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'time': ts,
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'ticker': ticker,
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'net_call_premium': round(cumulative['net_call_premium']),
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'net_put_premium': round(cumulative['net_put_premium']),
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'call_volume': round(call_volume),
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'put_volume': round(put_volume),
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'net_volume': round(net_volume),
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})
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# Sort the results list by time.
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res_list.sort(key=lambda x: x['time'])
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# Retrieve SPY price list data (using asyncio or fallback to local file).
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price_list = asyncio.run(get_stock_chart_data('SPY'))
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if len(price_list) == 0:
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with open("json/one-day-price/SPY.json", "r") as file:
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price_list = orjson.loads(file.read())
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# Append closing prices to the market tide data.
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data_with_close = add_close_to_data(price_list, res_list)
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# Ensure that every minute until 16:05 is present in the data.
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fields = ['net_call_premium', 'net_put_premium', 'call_volume', 'put_volume', 'net_volume', 'close']
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last_time = datetime.strptime(data_with_close[-1]['time'], "%Y-%m-%d %H:%M:%S")
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end_time = last_time.replace(hour=16, minute=5, second=0)
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while last_time < end_time:
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last_time += timedelta(minutes=1)
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data_with_close.append({
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'time': last_time.strftime("%Y-%m-%d %H:%M:%S"),
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'ticker': 'SPY',
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**{field: None for field in fields}
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})
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return data_with_close
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def get_sector_data(sector_ticker,interval_1m=True):
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res_list = []
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@ -358,7 +234,7 @@ def get_top_tickers(sector_ticker):
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def get_market_flow():
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market_tide = get_sector_data(sector_ticker="SPY") #get_market_tide()
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market_tide = get_sector_data(sector_ticker="SPY")
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top_pos_tickers = get_top_tickers(sector_ticker="SPY")
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top_neg_tickers = sorted(get_top_tickers(sector_ticker="SPY"), key=lambda item: item['net_premium'])
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for rank, item in enumerate(top_neg_tickers, 1):
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@ -9,6 +9,7 @@ from dotenv import load_dotenv
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import os
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import re
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from statistics import mean
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from collections import defaultdict
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# Database connection and symbol retrieval
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@ -29,8 +30,7 @@ def get_total_symbols():
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return stocks_symbols + etf_symbols +index_symbols
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def save_json(data, symbol):
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directory = "json/options-stats/companies"
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def save_json(data, symbol, directory):
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os.makedirs(directory, exist_ok=True)
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with open(f"{directory}/{symbol}.json", 'wb') as file:
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file.write(orjson.dumps(data))
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@ -42,6 +42,134 @@ def safe_round(value):
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except (ValueError, TypeError):
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return value
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def add_close_to_data(price_list, data):
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for entry in data:
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formatted_time = entry['time']
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# Match with price_list
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for price in price_list:
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if price['time'] == formatted_time:
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entry['close'] = price['close']
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break # Match found, no need to continue searching
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return data
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def get_market_flow_data(ticker,interval_1m=True):
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res_list = []
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# Load the options flow data.
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with open("json/options-flow/feed/data.json", "r") as file:
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all_data = orjson.loads(file.read())
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# Load ETF holdings data and extract ticker weights.
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# Use a common dictionary to accumulate flows across all tickers.
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delta_data = defaultdict(lambda: {
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'cumulative_net_call_premium': 0,
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'cumulative_net_put_premium': 0,
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'call_ask_vol': 0,
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'call_bid_vol': 0,
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'put_ask_vol': 0,
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'put_bid_vol': 0
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})
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# Process each ticker's data using its weight.
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# Convert the weight percentage to a fraction.
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weight = 1 #ticker_weights[ticker] / 100.0 #ignore weights of sector
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# Filter data for the current ticker.
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ticker_data = [item for item in all_data if item.get('ticker') == ticker]
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ticker_data.sort(key=lambda x: x['time'])
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for item in ticker_data:
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try:
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# Combine date and time, then truncate seconds and microseconds.
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dt = datetime.strptime(f"{item['date']} {item['time']}", "%Y-%m-%d %H:%M:%S")
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dt = dt.replace(second=0, microsecond=0)
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# Adjust to the start of the minute if using 1-minute intervals.
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if interval_1m:
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minute = dt.minute - (dt.minute % 1)
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dt = dt.replace(minute=minute)
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rounded_ts = dt.strftime("%Y-%m-%d %H:%M:%S")
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# Extract metrics.
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cost = float(item.get("cost_basis", 0))
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sentiment = item.get("sentiment", "")
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put_call = item.get("put_call", "")
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vol = int(item.get("volume", 0))
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# Update metrics, scaled by the ticker's weight.
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if put_call == "Calls":
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if sentiment == "Bullish":
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delta_data[rounded_ts]['cumulative_net_call_premium'] += cost
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delta_data[rounded_ts]['call_ask_vol'] += vol
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elif sentiment == "Bearish":
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delta_data[rounded_ts]['cumulative_net_call_premium'] -= cost
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delta_data[rounded_ts]['call_bid_vol'] += vol
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elif put_call == "Puts":
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if sentiment == "Bullish":
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delta_data[rounded_ts]['cumulative_net_put_premium'] += cost
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delta_data[rounded_ts]['put_ask_vol'] += vol
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elif sentiment == "Bearish":
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delta_data[rounded_ts]['cumulative_net_put_premium'] -= cost
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delta_data[rounded_ts]['put_bid_vol'] += vol
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except Exception as e:
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print(f"Error processing item: {e}")
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# Calculate cumulative values over time.
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sorted_ts = sorted(delta_data.keys())
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cumulative = {
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'net_call_premium': 0,
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'net_put_premium': 0,
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'call_ask': 0,
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'call_bid': 0,
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'put_ask': 0,
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'put_bid': 0
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}
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for ts in sorted_ts:
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cumulative['net_call_premium'] += delta_data[ts]['cumulative_net_call_premium']
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cumulative['net_put_premium'] += delta_data[ts]['cumulative_net_put_premium']
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cumulative['call_ask'] += delta_data[ts]['call_ask_vol']
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cumulative['call_bid'] += delta_data[ts]['call_bid_vol']
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cumulative['put_ask'] += delta_data[ts]['put_ask_vol']
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cumulative['put_bid'] += delta_data[ts]['put_bid_vol']
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call_volume = cumulative['call_ask'] + cumulative['call_bid']
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put_volume = cumulative['put_ask'] + cumulative['put_bid']
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net_volume = (cumulative['call_ask'] - cumulative['call_bid']) - (cumulative['put_ask'] - cumulative['put_bid'])
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res_list.append({
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'time': ts,
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'net_call_premium': round(cumulative['net_call_premium']),
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'net_put_premium': round(cumulative['net_put_premium']),
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'call_volume': round(call_volume),
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'put_volume': round(put_volume),
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'net_volume': round(net_volume),
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})
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# Sort the results list by time.
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res_list.sort(key=lambda x: x['time'])
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# Get the price list for the sector ticker.
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with open(f"json/one-day-price/{ticker}.json", "r") as file:
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price_list = orjson.loads(file.read())
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# Append closing prices to the data.
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data = add_close_to_data(price_list, res_list)
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fields = ['net_call_premium', 'net_put_premium', 'call_volume', 'put_volume', 'net_volume', 'close']
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last_time = datetime.strptime(data[-1]['time'], "%Y-%m-%d %H:%M:%S")
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end_time = last_time.replace(hour=16, minute=0, second=0)
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while last_time < end_time:
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last_time += timedelta(minutes=1)
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data.append({
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'time': last_time.strftime("%Y-%m-%d %H:%M:%S"),
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**{field: None for field in fields}
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})
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return data
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async def main():
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@ -52,6 +180,7 @@ async def main():
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for symbol in tqdm(total_symbols):
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try:
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#Start of daily stats
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call_premium = 0
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put_premium = 0
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call_open_interest = 0
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@ -131,13 +260,21 @@ async def main():
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}
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if aggregate:
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save_json(aggregate, symbol)
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save_json(aggregate, symbol,"json/options-stats/companies")
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else:
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os.remove(f"json/options-stats/companies/{symbol}.json")
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#End of daily stats
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flow_data = get_market_flow_data(symbol)
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if flow_data:
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save_json(flow_data, symbol,"json/market-flow/companies")
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else:
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os.remove(f"json/market-flow/companies/{symbol}.json")
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except:
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try:
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os.remove(f"json/options-stats/companies/{symbol}.json")
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os.remove(f"json/market-flow/companies/{symbol}.json")
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except:
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pass
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31
app/main.py
31
app/main.py
@ -4087,6 +4087,37 @@ async def get_data(api_key: str = Security(get_api_key)):
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headers={"Content-Encoding": "gzip"}
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)
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@app.post("/ticker-flow")
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async def get_data(data:TickerData, api_key: str = Security(get_api_key)):
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ticker = data.ticker.upper()
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cache_key = f"ticker-flow-{ticker}"
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cached_result = redis_client.get(cache_key)
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if cached_result:
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return StreamingResponse(
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io.BytesIO(cached_result),
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media_type="application/json",
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headers={"Content-Encoding": "gzip"}
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)
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try:
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with open(f"json/market-flow/companies/{ticker}.json", 'rb') as file:
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res = orjson.loads(file.read())
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except:
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res = []
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data = orjson.dumps(res)
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compressed_data = gzip.compress(data)
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redis_client.set(cache_key, compressed_data)
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redis_client.expire(cache_key,2*60)
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return StreamingResponse(
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io.BytesIO(compressed_data),
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media_type="application/json",
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headers={"Content-Encoding": "gzip"}
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)
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@app.get("/potus-tracker")
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async def get_data(api_key: str = Security(get_api_key)):
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cache_key = f"potus-tracker"
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